计算机应用与软件2024,Vol.41Issue(3) :246-252,282.DOI:10.3969/j.issn.1000-386x.2024.03.038

基于图神经网络的社会感知顺序推荐模型

SOCIAL PERCEPTION SEQUENTIAL RECOMMENDER SYSTEMS MODEL BASED ON GRAPH NEURAL NETWORK

张安勤 李然 田秀霞
计算机应用与软件2024,Vol.41Issue(3) :246-252,282.DOI:10.3969/j.issn.1000-386x.2024.03.038

基于图神经网络的社会感知顺序推荐模型

SOCIAL PERCEPTION SEQUENTIAL RECOMMENDER SYSTEMS MODEL BASED ON GRAPH NEURAL NETWORK

张安勤 1李然 1田秀霞1
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作者信息

  • 1. 上海电力大学计算机科学与技术学院 上海 201306
  • 折叠

摘要

由于用户的偏好是动态多变的,且受社交关系的影响,传统的推荐方法常常考虑不周全.针对此问题,提出一种基于图神经网络的社会感知顺序推荐模型(GASR)设计动态兴趣提取层来捕捉用户的动态偏好.同时设计社会感知层,利用图神经网络构建用户的社交关系图,使用注意力聚合方法来权衡不同朋友对用户偏好的影响.在两个实际数据集上的实验结果表明,该模型优于包括现有最新的社交推荐模型以及几个具有竞争性的基线模型.

Abstract

Since user preferences are dynamic and changeable and are affected by social relationships,traditional recommendation methods are often incomplete.To solve this problem,a social perception sequential recommendation model based on graph neural network(GASR)is proposed.The dynamic interest extraction layer was designed to capture the user's dynamic preferences.The social perception layer was designed and the graph neural network was used to construct the user's social relationship graph.The attention aggregation method was used to weigh the influence of different friends on user preferences.Experimental results on two actual data sets show that the model is superior to the latest social recommendation models and several competitive baseline models.

关键词

推荐系统/图神经网络/注意力机制/社交网络/门控循环单元

Key words

Recommendation system/Graph neural network/Attention mechanism/Social network/GRU

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基金项目

国家自然科学基金面上项目(61772327)

出版年

2024
计算机应用与软件
上海市计算技术研究所 上海计算机软件技术开发中心

计算机应用与软件

CSTPCD北大核心
影响因子:0.615
ISSN:1000-386X
参考文献量21
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